research paper on capital structure in india

Determinants of capital structure: a panel regression analysis of Indian auto manufacturing companies

• Research Paper
• Published: 07 August 2021
• Volume 23 , pages 338–356, ( 2021 )

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• S. Santhosh Kumar   ORCID: orcid.org/0000-0001-6108-4744 1 &
• C. Bindu 2  

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Capital Structure denotes the proportion of equities, preference share capital, long-term loans, debentures, retained earnings and other long-term sources of funds for business. The cost of these sources of funds, their tax advantages and legal implications are varied. The impact of these sources of funds on the value of the business is also different. Thus, the decision regarding the mix of different sources of funds in the capital structure is a challenging one for the practicing financial managers. The conventional theories of capital structure factored in some unrealistic assumptions to prove their propositions. But, in practice, there are a number of firm specific factors along with other quantifiable and non-quantifiable factors are influencing the capital structure decision of companies. This is an attempt to identify the firm specific factors influencing the capital structure decision of automobile manufacturing companies in India. The study employed panel regression analysis to identify the firm specific factors. Two variants of capital structure ratio such as Long-term Debt to Total Assets and Long-term Debt to Equity are tried in the panel data. It is found that firm size, profitability, tangibility, growth in assets and interest coverage are jointly influencing the capital structure decision of the auto companies.

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Introduction

The composition of debt and equity in the capital structure of business undertakings is a financial decision that determines their fortune as it has significant influence in determining the discounting factor for capital budgeting decisions and market value of firms (Durand 1952 ; Modigliani and Miller 1963 ). Thus, capital structure decision becomes a challenging one for the practicing financial managers. Naturally, the managers might look for an optimum or an appropriate capital structure that maximizes the value of the firm (Nenu et al. 2018 ; Modugu 2013 ). If constant cost of debt is true as assumed by Durand in NI approach ( 1952 ), there is an advantage in using more and more debt. But, if the cost of equity is increasing with the increased use of debt, as assumed by Durand in NOI approach ( 1952 ), the advantage of using more debt may be neutralized. Moreover, as long as corporate tax exists, the interest tax shield advantage from the use of debt is also to be factored in capital structure decisions. The discounted value of the interest tax shield resulting from the use of debt in the capital structure is confirmed as an advantage that increases the value of the firm by the seminal work of Modigliani and Miller ( 1963 ) So, the questions such as; (1) how the capital structure composition is determined by companies? and (2) what are the factors influencing the decision? are relevant for the practicing financial managers. Since this decision must maximize the worth of shareholders, the composition of capital that maximizes the value of the firm cannot be compromised Chen and Chen ( 2011 ). Therefore, knowing the determinants of capital structure is vital for choosing an appropriate capital structure. Subsequent to the pioneering works of Modigliani and Miller ( 1958 , 1963 ) and Durand ( 1952 ), many scholars viz., Jensen and Meckling ( 1976 ), Myers ( 1984 ), Kester ( 1986 ), Titman and Wessels ( 1988 ), Rajan and Zingales ( 1995 ), Booth et al. ( 2001 ), Dogra and Gupta ( 2009 ) have analyzed capital structure decision of firms in different geo contexts to trace the factors that influence the decision. However, a consensus in this regard is still not achieved. So, empirical studies on capital structure decision never end.

In practice, the capital structure decision is dependent on several factors that are either quantifiable or not. Similarly, industry related differences in the choice of components and the proportion of debt and equity in the capital structure are confirmed in many studies (Eldhose and Kumar 2019 ; Talberg et al. 2008 ; Das and Roy 2007 ). Companies with regular demand for their goods and thereby less volatile regular cash flows may go for more debt than those with irregular demands and volatile cash flows. The automotive industry covers a wide range of companies and organizations involved in the design, development, manufacture, marketing and selling of motor vehicles, towed vehicles, motorcycles and mopeds. It is one of the world's most important economic sectors in terms of its revenue size. They are subject to wide fluctuations in demand caused by the changes in macro and micro economic factors. The automobile industry in India is one of the largest and fastest growing industries globally, with a production of 29 million vehicles in the year 2018 and a growth rate of 14.8% compared to the previous year (IBEF 2019 ; SESEI 2018 ). This industry, after de-licensing in India in July 1991, has shown spectacular growth rate. The CAGR of 7.01% in domestic sales for 5 years ending on 2019 is a sign of consistency in the growth of the industry (IBEF 2019 ). The GDP contribution of the industry is nearly 7% and its share in the India’s manufacturing GDP is nearly 22% (IBEF 2019 ). The value addition by these companies to the stakeholders is therefore significant. The seasonal and capital intensive nature of this industry distinguishes its capital structure decision from that of the companies in other industries. This study is an exclusive attempt to identify and model the determinants of capital structure of automobile manufacturing companies in India.

Theoretical framework for the study

Capital structure theories.

The capital structure theories are examining the relationship between leverage ratio and value of the firm. While a set of capital structure theories (relevance theories) establish that optimum capital structure that maximizes the value of the firm can be obtained by the use of hundred per cent debt, another set of theories (irrelevance theories) proves that the value of the firm is constant for all degrees of leverage. The four major capital structure theories are (1) Net Income Approach (NI), (2) Net Operating Income Approach (NOI) both by Durand in 1952, (3) Modigiliani and Miller Approach (MM) in 1958 and (4) Traditional Approach (Khan and Jain 2019 ; Pandey 2010 , Modigliani and Miller 1958 ; Durand 1952 ). The NI approach argues that the debt-equity proportion impacts the overall cost of capital (Ko) and value of the firm (V). On the contrary, the NOI approach establishes that the Ko and V are constant for all degrees of leverage or debt-equity mix. The MM approach, by assuming perfect capital markets, supports the NOI approach for the constant Ko and V for all degrees of leverage by providing a behavioral justification for the constant Ko and V. The Traditional approach stands mid-way between NI and NOI approaches supporting the impact of leverage on the firm value up to a certain level and the neutral impact of leverage beyond that level. Modigliani and Miller ( 1963 ) subsequently modified their capital structure irrelevance proposition for taxes. They come to the conclusion that interest on debt being a tax deductible expense, and the firms using more debt can effectively reduce their tax bill, and thereby levered firms can have more value than unlevered firms. All the above theories are established by factoring in only two variables, the leverage ratio and the value of the firm, keeping the effect of all other factors constant. Thus, these theories are criticized on many grounds especially for the unrealistic assumptions they stick on for establishing the respective propositions. In the real world context, the capital structure decisions are influenced by a number of factors other than the debt-equity mix and its impact on the value of firm.

Theoretical determinants of capital structure

There are certain theories which explain the factors that influence the practicing managers in capital structure decisions. They are the trade-off theory, signaling theory, pecking order theory and agency theory. The trade-off theory of capital structure postulates that through capital structure decisions, managers attempt to balance the benefits of interest tax shields against the present value of the possible costs of financial distress on account of those decisions (Kraus and Litzenberger 1973 ). Interest tax shield is an advantage on account of the reduction in corporate taxes payable as interest on debt is a tax deductible expenditure. The financial distress costs consist of higher interest rate associated with the increased use of debt and the consequent bankruptcy cost. When the use of debt increases, the risk perception of debt holders will increase resulting in higher cost of capital of debt. The bankruptcy cost is the cost directly incurred when the perceived probability that the firm will default on debt financing is greater than zero (Kraus and Litzenberger 1973 ; Chen and Chen 2011 ). Thus, the trade-off behavior of the financial managers to have a balance between interest tax shield advantage and the financial distress costs is the essence of trade-off theory.

There is an information asymmetry about firm’s prospects between the management and the shareholders. The signaling theory bases these information asymmetries between company managements and shareholders. Managers are informed more about the business operations (insider information) and the future prospects of a company than the shareholders. This is called information asymmetry. If managers have insider information, their choice of capital structure will signal some information to the market. Increase in debt is a positive sign that managers are confident about the future earnings (Ross 1977 ). In brief, a firm with prospects would avoid selling shares and vice versa. Thus, as per signaling theory, debt issues are considered as good news and share issues as bad news.

According to the pecking order theory, the decision on sources of financing depends on the preference order followed by the managers in practice. The theory argues that the firms prefer internal financing to external financing (Myers and Majluf 1984 ). If internal funds are not enough to finance investment opportunities, firms may acquire external financing (Luigi and Sorin 2009 ). Among the external sources of finance, first preference is for debt followed by preference shares, convertible bonds and finally by new equity shares (Chen and Chen 2011 ; Sheikh and Wang 2010 ). Thus, pecking order theory implies that managers will work down a pecking order by opting to issue the cheapest form of financing as part of having least resistance.

The essence of agency theory is that the managers will not always act in the best interest of the shareholders (Jensen and Meckling 1976 ; Myers 1977 ). The managers are tempted to pursue the profits of the firms they manage to their own personal gain at the expense of the shareholders. Similarly, the debt provides shareholders with the incentive to invest sub-optimally. If an investment yields higher than the cost of debt, the net benefits out of it accrue to the shareholders. Conversely, if the investment fails, the shareholders enjoy limited liability by exercising their right to walk away (Harris and Raviv 1991 ). Thus, higher profit earning firms retain more debt.

The above four theories are the strong indicators of the factors stepping in while concluding the debt-equity ratio decision by companies. Moreover, the empirical studies over time have factored in some other related variables in the debt-equity decision which are reviewed below.

Empirical evidences on factors determining capital structure

Extensive literatures on the factors influencing capital structure of companies across the world are available. But these studies differ in their findings regarding the combinations of factors that are significant in determining the capital structure of the companies. Most of these studies confirmed size of the firm, profitability, growth in assets and tangibility as the significant determinants of capital structure (Juniarti and Utami 2017 ; Babu and Chalam 2016 ; Muller 2015 ; Ozkan 2001 ; Bhole and Mahakud 2004 ; Basu and Rajeev 2013 ; Rajan and Zingales 1995 ; Indi 2015 ; Poddar and Mittal 2014 ; Cekrezi 2013 ; Booth et al. 2001 ; Ashraf and Rasool 2013 ; Pinková 2012 ; Awan et al. 2011 ; Jagannathan and Suresh 2017 ; Amsaveni and Gomathi 2010 ; Demirguc-Kunt and Maksimovic 1999 ; Titman and Wessels 1988 ; Hackbarth et al. 2007 ; Ai-Ajmi et al. 2009 ; Bhaduri 2002 ; Pandey 2005 ). However, these studies are contradictory regarding the number determinants, magnitude of determinants and also regarding the direction of relationship between the different determinants and the capital structure. Negative relationship between capital structure and profitability are established by Juniarti and Utami ( 2017 ), Babu and Chalam ( 2016 ), Ashraf and Rasool ( 2013 ), Rajan and Zingales ( 1995 ), Booth et al. ( 2001 ); Ozkan 2001 ; Bhole and Mahakud ( 2004 ). Size of the firm was observed to be positively related to capital structure (Juniarti and Utami 2017 ; Poddar and Mittal 2014 ; Muller 2015 ; Rajan and Zingales 1995 ; Sahoo and Omkarnath 2005 ). Some studies found negative relationship of capital structure with tangibility (Juniarti and Utami 2017 ; Babu and Chalam 2016 ). Studies are contradicting by finding both positive and negative relationship between capital structure and interest coverage ratio (Poddar and Mittal 2014 ; Brigham and Daves 2007 ; Harris and Raviv 1991 ; Brigham and Daves 2007 ; Madan 2007 ; Rajan and Zingales 1995 ; Eriotis et al. 2007 ; Suhaila and Mahmood 2008 ). Non-debt tax shield is found as a significant determinant of capital structure in many studies. But it is not a relevant one as per some other studies in the literature (Kumar and Bindu 2018 ; Babu and Chalam 2016 ; Ashraf and Rasool 2013 ; Ozkan 2001 ; Bhole and Mahakud 2004 ; Sahoo and Omkarnath 2005 ). Yadav ( 2014 ) found agency costs, taxes, and asymmetric information as important determinants of capital structure. Dividend payout ratio has also been found in studies as a determinant of capital structure (Ahmed 2012 ; Jagannathan and Suresh 2017 ).

The review of the previous studies on capital structure reveals that the relationship between capital structure and the influencing factors vary in their magnitude of influence and direction of influence. Moreover, differences in capital structure decisions are also found in terms of the nature of industry and size of the company. Therefore, identifying the determinants of capital structure and their magnitudes of influence irrespective of the nature of the industry and size of the company is inadequate. In short, industry specific studies alone can clearly portrait the determinants of capital structure and their interdependence.

Materials, methods and model specification

All the 17 BSE listed automobile manufacturing companies in India coming under the three segments such as (a) Two and three wheelers (7 companies), (b) cars and utility vehicles (4 companies) and (c) commercial vehicles (6 companies) are considered for the analysis. The Annual Reports of these automobile companies formed the primary source of the data. These data were collected from PROWESS date base of the Center for Monitoring Indian Economy. Ten years’ data from 2008 to 2017 are considered for the analysis. So, altogether, a balanced panel with 170 observations is used for the analysis.

The study employs panel data regression for the measurement of the relationship among the variables. In order to estimate the effects of predictor variables on the target variable, two estimation models were used viz., Fixed Effects Model (FEM) and Random Effects Model (REM). Hausman Specification Test is carried out to determine which model is suitable in each case. Initially, the Jarque Berra test is carried out to check the normality of data and the Philips-Perron Fisher Chi-square Unit Root Test to test the stationarity of the data. Linear relationship between variables is checked by correlation. Variance Inflation Factor (VIF) is calculated to check the multicollinearity among the explanatory variables. The analysis was carried out in EViews 8 software. The capital structure ratio is the dependent variable, and the five independent variables chosen from the literature are size of the firm, profitability, tangibility, growth in assets, and interest coverage ratio. The two variants of the capital structure ratio applied and the detailing of the independent variables are given Table 1 .

Model specification

The two variants of capital structure, ‘DTA’ and ‘DE’ are regressed separately with the five independent variables. The following regression model is used to test the relationship between capital structure and its determinants.

where β 0 coefficient of intercept (constant), β 1 coefficient of size, β 2 coefficient of profitability, β 3 coefficient of tangibility, β 4 coefficient of growth in assets, β 5 coefficient of interest coverage, U it the error term.

The capital structure defined as Long-Term Debt to Total Assets (DTA) or Long-Term Debt to Equity (DE) of automobile companies in India is jointly determined by their (a) firm size (b) profitability (c) tangibility (d) growth in assets, and (e) interest coverage.

Results and discussion

All automobile companies, descriptive statistics, stationarity and correlation of variables.

The descriptive statistics of the variables are shown in Table 2 . The Philips-Perron Chi-square test applied found that all the variables stationary at level itself (Table 3 ). The correlations among the dependent and independent variables are less than 0.50 (Table 4 ). Except ‘tangibility’ and ‘interest coverage’, all other independent variables are inversely related to ‘DTA’ (one of the variants of the dependent variable). When we consider the other variant of the dependent variable (i.e., DE), all the determinants except ‘tangibility’ are positively correlated. In the Indian context, the previous studies have inferred both positive and negative correlation between the debt-equity ratio and its determinants (Sofat and Singh 2017 ; Babu and Chalam 2016 ; Mittal and Kumari 2015 ; Yadav 2014 ; Ahmed 2012 ; Indi 2015 ). In short, no consistency is observed in the direction of correlation between the dependent and independent variables among the empirical studies. This is also true in the case of the correlation between the independent variables. Among the independent variables, size of firm is positively related to ‘profitability’, ‘growth in assets’ and ‘interest coverage’. But, it is negatively correlated to tangibility. Thus, the literature supports both positive and negative correlations between “size’, ‘profitability’, ‘growth in assets’ and ‘interest coverage’.

• Capital structure

The preferred model is REM (Hausman p value > 0.050) when the capital structure variant DTA is regressed against the independent variables (Table 5 ). Out of the five regressors, only two variables ‘tangibility’ and ‘interest coverage’ are significant at 5% level since the probability of t -statistic is less than 0.05. Both the variables have positive relationship with DTA. Since the p value of F test is less than 0.05, the model with all the independent variables is explaining the DTA variant of capital structure ratio of the automobile companies in India. Since the VIF value is 1.093, there is no multicollinearity problem among the variables in the model. Then, the model can be represented as:

When the direction of relationship is considered, both ‘tangibility’ and interest coverage ratio are positively related to capital structure. Similar relationship of tangibility to capital structure is confirmed by Jensen and Meckling ( 1976 ), Myers ( 1977 ), Korajczyk and Levy ( 2003 ), Frank and Goyal ( 2009 ) and Hanousek ( 2011 ). However, the positive relationship of interest coverage ratio is contradictory to the pecking order theory (Myers and Majluf 1984 ) and the study by Harris and Raviv ( 1991 ).

The panel regression results (REM) obtained by regressing the capital structure variant ‘DE’ with the five independent variables (Table 6 ) reveal that none of the predictor variable is significantly influencing the capital structure ratio represented by DE as the p values of all the coefficients are greater than 0.05. Moreover, the probability of F -statistic (> 0.05) is also indicates that the model lacks overall significance.

Two and three wheelers

Among the 17 companies, seven are in the two and three wheeler segment. The descriptive statistics of all the variables for the seven companies are given in Table 7 . The data are stationary at level. The coefficients of inter correlation between the variables are calculated, and the results are shown in Table 8 .

The regression results of the seven companies considering DTA and DE as capital structure ratio proxies are given in Tables 9 and 10 , respectively. The appropriate model in both the cases is REM (Hausman test > 0.05). Tangibility is the only significant determinant of capital structure when the proxy is ‘DTA’, and it is positively related to DTA. But, the F test results indicate that the model with all the variables is explaining the capital structure ratio as the p value of the test is < 0.05. The VIF value of 1.231 excludes any multicollinearity issue among the variables.

Then, the model can be represented as

Profitability is the significant determinant of capital structure when the proxy is ‘DE’ and it is positively related to capital structure (Table 10 ). All other predictors are not significant at 5% significance level. The model has overall significance since p value of F -statistic is less than 0.05. The model explains 23% variations in ‘DE’. There is no multicollinearity problem among the variables as the VIF value is less than 5.

Cars and utility vehicles

Four companies out of the 17 companies are in the cars and utility vehicles segment. The descriptive statistics of all the variables of the segment are given in Table 11 . The data series of ‘DTA’, ‘size’, ‘tangibility’ and ‘growth in assets’ of the companies are not stationary at level. Thus, the first differenced data are used. The correlations between the variables are shown in Table 12 .

The regression results of the determinants of capital structure of the cars and utility vehicles are examined using random effects model (Hausman test value 0.895). The results for the capital structure proxy ‘DTA’ and ‘DE’ are given in Tables 13 and 14 . Since the number of cross sections is less than the number of independent variables, the period random effect alone is considered.

The variables ‘size’, ‘tangibility’ and ‘growth in assets’ are the significant determinants of capital structure (DTA) in the case of the cars and utility vehicles. While, size of the firm and growth in assets are negatively influencing the capital structure, tangibility has positive relationship. The model explains 67% variations in the capital structure represented by DTA. The model has overall significance since p value of F test is less than 0.05. Moreover, the variables are free from the multicollinearity problem (VIF value 3.117). Then, the model can be represented as below.

‘Tangibility’ is the significant determinant of capital structure when ‘DE’ is the proxy (Table 14 ). It has a negative relationship with capital structure. Since the p value of F test is greater than 0.05, the model lacks overall significance.

Commercial vehicles

Out of the 17 companies, 6 companies are in the commercial vehicles segment. The descriptive statistics of their data are shown in Table 15 . Some of the variables of these companies such as ‘DTA’, ‘size’ and ‘interest coverage’ are not stationary. They are subjected to first differencing. The coefficients of inter correlation between variables are shown in the Table 16 .

The ‘interest coverage’ is the only significant determinant of capital structure (DTA), and it has a negative relationship to capital structure. Since the value of R squared is 0.35, the model has reasonable explaining power. The p value of F test (0.001) reveals that the model has overall significance. The VIF value is 1.53. So there is no multicollinearity issue among the variables (Table 17 ).

Then, the model can be represented as:

None of the regressor is found significant when the DE variant is regressed in the case of the commercial vehicle companies. The p value of F -statistic is also not significant (Table 18 ).

Hypotheses testing results

The inferences made out of the hypotheses tests regarding the determinants of capital structure of automobile companies in India on the whole and segment-wise are detailed below in Table 19 .

Contribution to the existing literature

The results of the study are in line with the traditional theories of capital structure. The positive relationship of asset tangibility, profitability and interest coverage capacity to capital structure found in the study is in unison with the findings established by the Trade-off Theory of capital structure. Companies having more tangible assets may afford large amount of debt as tangible assets can provide better collateral requirements. Profitable organizations use more debt as they have a lower probability of bankruptcy. These firms can avail debt on more favorable terms than those with lesser profitability. The negative relationship of the size of the firm to the capital structure, found in the study, stands akin to the Pecking Order Theory of capital structure. Similarly, the negative relationship found between the growth of the firm and the capital structure supports the traditional Trade-off Theory.

Implications of the study

This study has laid down a platform for the financing decisions of companies by anchoring the determinants of capital structure of automobile manufacturing companies in India. The practicing managers can factor in these variables in their capital structure decision. The findings that the firm specific factors such as the size of the firm, profitability, asset tangibility, growth in assets and interest coverage ratio have material influence on capital structure decisions of automobile manufacturing companies in India is a conclusive evidence that would facilitate the managers in their value maximization process.

Capital structure decision still remains a core concern in corporate finance, even after the 60 years of the pivotal work of Modigliani and Miller ( 1958 ). The decision regarding the mix of different sources of funds in the capital structure is a challenging one for the financial managers as it is significantly influencing the value of companies. The conventional theories of capital structure decision factor in significant number of unrealistic assumptions to establish the propositions made as part of the theories (Ahmeti 2015 ; Gifford 1998 ). But, in practice, there are a number of firm specific factors along with other quantifiable and non-quantifiable external aspects are influencing the capital structure decision of companies (Basu and Rajeev 2013 ). The study identified the firm specific determinants of capital structure of all automobile manufacturing companies as one panel and for each segment of the industry separately. It is found that the firm size, profitability, tangibility, growth in assets and interest coverage are jointly influencing the capital structure decision of the companies. This finding is helpful to the practicing managers in the capitalization makeup decision of new and existing companies.

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Santhosh Kumar, S., Bindu, C. Determinants of capital structure: a panel regression analysis of Indian auto manufacturing companies. J. Soc. Econ. Dev. 23 , 338–356 (2021). https://doi.org/10.1007/s40847-021-00159-9

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The impact of Covid-19 on the capital structure in emerging economies: evidence from India

Asian Journal of Accounting Research

ISSN : 2459-9700

Article publication date: 5 December 2022

Issue publication date: 15 June 2023

Capital structure is an important corporate financing decision, particularly for companies in emerging economies. This paper attempts to understand whether the pandemic had any significant impact on the capital structure of companies in emerging economies. India being a prominent emerging economy is an ideal candidate for the analysis.

Design/methodology/approach

The study utilizes three leverage ratios in an extended market index, BSE500, for the period 2015–2021. The ratios considered are short-term leverage ratio (STLR), long-term leverage ratio (LTLR) and total leverage ratio (TLR). A dummy variable differentiates the pre-epidemic (2015–2019) and pandemic (2020–2021) period. Control variables are used to represent firm characteristics such as growth, tangibility, profit, size and liquidity. Dynamic panel data regression is employed to address endogeneity.

The findings point out that Covid-19 has had a significant, negative effect on LTLR, while the impact on STLR and TLR was insignificant. The findings indicate that companies based in a culturally risk-averse environment, such as India, would reduce the long-term debt to avoid bankruptcy in times of uncertainty.

Research limitations/implications

The study covers the impact of the pandemic on Indian companies. Hence, generalization of the findings to global context might not be valid.

Practical implications

To maintain economic growth in the post-crisis period, Indian policymakers should ensure accessibility to low-cost capital. The findings provide impetus to deepen the insignificant corporate bond market in India for future economic revival.

Originality/value

Developing countries are struggling to revive the economies postpandemic. This is particularly true for Asian economies which are heavily reliant on banks for survival. This research finds evidence to utilize bond market as a source of raising capital for economic revival.

• Capital structure
• Bankruptcy risk
• Emerging economies

Prakash, N. , Maheshwari, A. and Hawaldar, A. (2023), "The impact of Covid-19 on the capital structure in emerging economies: evidence from India", Asian Journal of Accounting Research , Vol. 8 No. 3, pp. 236-249. https://doi.org/10.1108/AJAR-05-2022-0144

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Copyright © 2022, Nisha Prakash, Aditya Maheshwari and Aparna Hawaldar

Published in Asian Journal of Accounting Research . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Maintaining an optimal capital structure is a fundamental function of the corporate finance team. Companies vary in their approach to capital structure decision-making. The ideal capital structure is a much-debated area among academicians and practitioners. One view proposes the irrelevancy of capital structure in determining the value of a company while a contrary view shows how capital structure affects risk and hence the firm value. Determining the proportion of debt in capital structure is a critical decision for enterprises, particularly in emerging economies. Though debt is a cheaper form of capital offering tax benefits, higher debt reduces the liquidity of companies making it difficult to survive during contractions.

Capital structure theories on optimum capital structure have garnered much attention from academicians and practitioners alike since the seminal work conducted by Modigliani and Miller (1958 , 1963) . Extensive research has been carried out to understand the factors impacting the capital structure of companies. Empirical studies have reaffirmed that firm characteristics, the institutional settings and the macroeconomic uncertainty are strong influencers of capital structure ( Demirgüç-Kunt and Maksimovic, 1999 ; Graham et al. , 2015 ). The approach to capital structure and leverage also differs between countries based on their risk appetite.

Empirical studies indicate that an external macroeconomic or financial uncertainty will influence the capital structure of companies in diverse ways ( Kenc and Dibooglu, 2010 ; Atici and Gursoy, 2011 ). Generally, during any crisis, expected returns will weaken as risk and uncertainty increase. The borrowers and lenders become hesitant to advance funds for long-term projects. Considering the higher probabilities of default during a crisis, the lenders would demand a higher term premium on their lending. This high-cost of long-term borrowing makes them less appealing compared to short-term borrowing ( Demirgüç-Kunt et al. , 2020 ). According to the economic theory, the degree of impact of any external shock on the leverage of companies depends on the features of the financial systems of that country as well as on the institutional ecosystem in which the companies function. Whenever there are higher uncertainty and risk, the reduction of debt maturities and the resultant change in leverage is most likely to occur in ecosystems where bankruptcy laws and procedures are expensive. As the risk or uncertainty build-up and business potentials become more and more ambiguous, the companies may also desire to reduce their debt ( Demirgüç-Kunt et al. , 2020 ). Consequently, during period of turbulent economy there may be a drop in debt levels.

While research on the capital structure decisions during uncertainty in developed markets has been extensive ( de Jong et al. , 2008 ; Rajan and Zingales, 1995 ), research in the context of emerging economies is still nascent. The debt levels of emerging economies are fast approaching that of the developed countries. The findings of the developed economies cannot be extended to emerging markets as the dynamics, practices and the approach to capital structure decisions are distinct. Researchers argue that the capital structure decisions could be influenced by the regulatory framework, tax systems and corporate governance requirements ( Bhaduri, 2002 ). Hence, it is essential to study individual economies rather than aggregating as a group. This study analyses the capital structure decisions in the Indian context. Postliberalization of 1991, Indian companies overleveraged to raise capital to meet market requirements ( Dawar, 2014 ; Ganguli, 2013 ). The pandemic raised many questions about how the economic and financial uncertainty affects corporate decision-making. This study attempts to answer one of these questions: whether the pandemic had a significant impact on the capital structure of Indian companies? The study considers nonfinancial companies included in BSE Sensex 500 index.

This research enhances the literature by developing an understanding of how the pandemic impacted the capital structure of Indian-listed nonfinancial companies and attempts to identify changes in leverage ratio during uncertainty. The paper is organized into sections covering literature review, research methodology, results, discussion and conclusion.

2. Theoretical framework and literature review

In this paper, we attempt to understand whether the companies responded differently to its short-term debt (STD) and long-term debt (LTD) decisions during the pandemic. This section briefly explains the theories followed by the empirical studies, which explain the factors influencing capital structure decisions.

2.1 Theoretical framework

Initiated by Modigliani and Miller (MM) (1958) , the current framework for understanding the factors influencing debt levels was set by the pioneering work conducted by many researchers. Popularly called MM theory, the initial theory postulates that if one excludes the influence of taxes, information asymmetries and transaction costs, the capital structure does not influence the firm value. They concluded that the value of a firm under these conditions remain the same whether the firm is unleveraged or leveraged. The absence of taxes made the initial model unrealistic. In subsequent studies, the researchers introduced modified MM models including the impact of tax ( Miller, 1977 ; Modigliani and Miller, 1963 ). The tax-based models recommend profitable companies to raise more debt as the presence of debt was found to enhance the firm value. MM models became popular for its optimal capital structure at which the cost of financing was the lowest.

The theories to explain capital structure can be grouped into two broad approaches, namely, trade-off theory and pecking order theory. Each theory explains the decision between debt and equity by understanding its effect on firm value from different viewpoints. Trade-off theory suggests that optimal capital structure would depend on the balancing of firm costs and debt benefits. For instance, the classical trade-off theory weighed the tax shield of debt with bankruptcy costs to determine the optimal debt levels ( Kraus and Litzenberger, 1973 ). According to this theory, the optimal debt level is when the tax benefits of debt neutralize the cost of bankruptcy. Copeland and Weston (1983) also argued that bankruptcy cost could be used to explain the differences in capital structure. Another variation of trade-off theory postulates that the agency structure and related costs would have a significant influence on the capital structure ( Jensen and Meckling, 1976 ). This theory identified the possibility of disputes between the shareholders and their agents. Jensen (1986) proposed that the agency cost can be reduced by issuing equity to managers or raising the volume of debt in the capital structure.

Another approach to explain the capital structure was based on information differences between different stakeholders ( Leland and Pyle, 1977 ). Pecking order theory utilized the concept of information asymmetries to explain the capital structure ( Myers and Majluf, 1984 ). The capital structure can be viewed as an indication given by manager to investors or as a way of reducing inefficiencies due to information asymmetry. According to this theory, companies prefer internal sources, debt and equity in that order to minimize the cost of information asymmetries. Harris and Raviv ( Harris and Raviv, 1990 ) extended the information asymmetry to suggest that debt acts as a disciplining device for managers. The assumption of this theory is that managers are reluctant to publicize information detrimental to their position. In case of debt default, the firm is forced into liquidation which makes information publicly available, reducing the asymmetry. Information asymmetry has also been used by researchers ( Diamond, 1989 ; Hirschleifer and Thakor, 1989 ) to explain the preference for debt or equity for specific projects. Management typically prefers debt to finance high risk projects, instead of equity financing. As companies mature, their credit ratings improve which lowers the cost of debt. Therefore, the researchers argued that younger firms will have lower debt compared to older ones.

In spite of the large volume of theoretical and analytical studies testing these theories in different geographies and contexts, no agreement has been reached on their relevance. The results are inconclusive and indicate that the relevance of these theories is highly contextual ( Graham, 2000 ; Leary and Roberts, 2010 ).

2.2 Literature review

Several empirical studies have linked capital structure to internal and external factors. This section provides a brief outline of the empirical studies covering capital structure and its determinants. In this paper, we adopt the view followed by the existing literature that the preference between the different financing options depends on firm characteristics. The primary factors suggested by the literature are asset tangibility, tax levels, size, profitability, growth, liquidity, cash flow and the industry ( DeAngelo and Masulis, 1980 ; Rajan and Zingales, 1995 ; Stulz, 1990 ; Titman and Wessels, 1988 ). Since we are doing a country-specific study, we exclude the tax levels in our analysis.

Asset tangibility has a significant positive impact on long-term leverage.

Asset tangibility has no significant impact on short-term leverage.

Size of a company has a significant positive impact on long-term leverage.

Size of a company has a significant impact on short-term leverage.

Profitability has no significant impact on long-term leverage.

Profitability has no significant impact on short-term leverage.

Growth opportunities have no significant impact on long-term leverage.

Growth opportunities have no significant impact on short-term leverage.

Liquidity has a significant positive impact on long-term leverage.

Liquidity has a significant positive impact on short-term leverage.

Pandemic had a significant negative impact on total leverage.

Pandemic had a significant negative impact on long-term leverage.

Pandemic had a significant negative impact on short-term leverage.

Literature during the pandemic years have focused on the challenges faced by organizations in maintaining the target leverage ratio ( Vo et al. , 2022 ), impact of the capital structure on firm survival during crisis ( Arianpoor and Tajdar, 2022 ) and the capital structure and its impact on firm recovery strategies ( Yost et al. , 2021 ). As organizations stayed away from debt, researchers also explored non-conventional sources of capital such as crowdfunding to raise capital during crisis years. A few country-specific studies have analysed the impact of the pandemic on the capital structure of enterprises. The findings of these studies are varied depending on the country characteristics. For instance, empirical studies conducted on European countries have shown a significant increase in debt levels during 2020 while no significant changes in debt levels were reported in a few other countries. Hence, it is essential to conduct country-specific studies to understand the impact of pandemic on the capital structure.

3. Research methodology

Based on the theoretical framework and empirical studies, we have considered firm-specific growth, tangibility, size, profitability and liquidity. A dummy variable is used to differentiate between the pre-pandemic and pandemic period. The variables used for the study are described in Table 1 .

To understand the impact of Covid-19 on the leverage ratio, we consider the firm-specific characteristics of the constituents of an index, BSE 500. BSE 500 represents about 93% of the US$3.5tn market capitalization of Bombay Stock Exchange (BSE). BSE is the eighth largest stock exchange in the world. BSE 500 covers all the major industries of the Indian economy and hence is representative of corporate India as existing research shows that the basis of capital structure decisions during crisis remains consistent across markets with similar institutional characteristics ( Alves and Francisco, 2015 ; Harrison and Widjaja, 2014 ). Hence, the findings of this study can be generalized and applicable to emerging economies similar to India.

Financial institutions are excluded because of their particular characteristics ( King and Santor, 2008 ). The period considered for analysis is 2015–2020. The descriptive statistics are provided in Table 2 . Debt formed only 25% of the assets, on average. It could be due to the shallow debt market in India which makes it difficult for companies to raise capital through debt instruments. The companies are heavily reliant on banks for raising debt capital. With the central bank tightening regulations around non-performing assets, banks are reluctant to give loans to companies.

3.1 Modelling

We have constructed three unbalanced panel data regression models. Each of these models would assess the impact of the pandemic on one of the three leverage ratios, namely, TLR, STLR and LTLR. However, endogeneity is a recurring problem in corporate financial data analysis. To account for endogeneity, this study utilizes a dynamic panel data regression model, a technique commonly employed in literature. Following the prevailing literature on the regressors of leverage, the dynamic panel data model can be written as follows: (1) C a p i t a l   S t r u c t u r e ( t )   = f ( C a p i t a l   S t r u c t u r e ( t − 1 ) ,   G r o w t h ,   T a n g i b i l i t y ,   S i z e ,   P r o f i t a b i l i t y ,   L i q u i d i t y ,   C o v i d − 19 )

Substituting the capital structure variables – TLR, LTLR and STLR – in (1) , the models can be rewritten as follows: (2) R 1 :   T L R t   = f   ( T L R t − 1 ,   G r o w t h ,   T a n g i b i l i t y ,   S i z e ,   P r o f i t a b i l i t y ,   L i q u i d i t y ,   C o v i d − 19 ) (3) R 2 :   S T L R t   = f   ( S T L R t − 1 ,   G r o w t h ,   T a n g i b i l i t y ,   S i z e ,   P r o f i t a b i l i t y ,   L i q u i d i t y ,   C o v i d − 19 ) (4) R 3 :   L T L R t   = f   ( L T L R t − 1 ,   G r o w t h ,   T a n g i b i l i t y ,   S i z e ,   P r o f i t a b i l i t y ,   L i q u i d i t y ,   C o v i d − 19 )

An appropriate diagnostic test was run to check for model fit and stability.

The results of the Levin–Lin–Chu unit-root test are provided in Table 3 ( Levin et al. , 2002 ). We reject the null hypothesis of non-stationarity and conclude that all the variables are stationary at level. We can now proceed to establish the long-term relationship between TLR and the independent variables.

The correlation between the variables given in Table 4 shows the absence of multicollinearity.

Now, we move to Step 3 i.e. building a dynamic unbalanced panel data regression model to determine the influence of Covid-19 on the leverage ratios. For each regression, we conduct the Hausman test to determine whether to consider fixed or random effect estimation models. The null hypothesis of the Hausman test is that the appropriate model is the random effect model ( Hausman, 1978 ). Depending on the p -value of the Hausman test, we proceed with a fixed- or random-effect model. According to the chi-square statistic and the p -value of the Hausman test as given in Table 5 , we reject the null hypothesis for regressions R1, R2 and R3 and proceed with the fixed effect estimation model.

The results of the fixed effect models are provided in Table 6 . Table 7 provides a summary of hypothesis testing.

5. Discussion

The regression models strongly suggest the reduction of debt levels by Indian companies during the pandemic. Consistent with our earlier expectations, any uncertainty reduces the confidence of companies to undertake capital expenditure projects. Further, companies would tend to pay back their debt to avoid bankruptcy during worsening times. The findings are in agreement with existing literature which suggests that bankruptcy costs have a strong influence on the leverage of companies ( Copeland and Weston, 1983 ; Kraus and Litzenberger, 1973 ). The results are also in agreement with empirical studies conducted during the 2008 financial crisis ( Proença et al. , 2014 ). Companies in India are heavily reliant on banks for raising capital; hence, debt is the primary choice of capital for future expansion. The preference of debt over equity is in agreement with the pecking order theory and other empirical studies.

Future growth opportunities are not a significant determinant of leverage. Companies with tangible assets are expected to lean towards debt as fixed assets are worth more than intangible assets during liquidation ( Huang, 2006 ; Williamson, 1988 ). As expected, results indicate that tangibility has a significant positive impact on the long-term debt preference of Indian companies. Companies with more fixed assets can use it as security to borrow debts which will reduce the borrowing costs, i.e. asset tangibility reduces the agency cost of debt making them more attractive. Hence, our results are in agreement with the agency theory ( Jensen and Meckling, 1978 ). It also supports some of the empirical studies in the literature ( Rajan and Zingales, 1995 ). Interestingly, the results indicate that collateral is a necessity for long-term borrowing in India. Similar to tangibility, firm size also has a positive influence on leverage, though not significant. The results support the findings of previous studies ( Wald, 1999 ). Return on assets has a significant, negative impact on debt levels, i.e. profitable companies tend to prefer equity over debt in India. Companies reporting consistent profits would find it easy to attract equity investors. The results are in line with existing literature ( Friend and Lang, 1988 ; Jensen, 1986 ; Wiwattanakantang, 1999 ). However, the impact is significant only for short-term leverage. Liquidity has a positive impact on long-term leverage and negative for short-term leverage. The reliance on external debt for short-term needs would be lower for liquid companies, whereas liquidity increases the credit worthiness for raising long-term debt. To summarize, the debt levels of companies in India are positively impacted by asset tangibility, size and liquidity, while profitability tends to lower leverage. In addition to all the control variables, an external crisis such as Covid-19 significantly lowers debt levels.

6. Policy implications

For consistent growth and development, it is essential to ensure that economies remain robust. Companies are major contributors in rebuilding economies post the pandemic. Hence, policies should cater to ensure their smooth operations and performance. One of the factors which impact the performance of companies is their capital structure decisions, both for long-term and short-term investments. These decisions are crucial, particularly during uncertainty as the natural tendency of firms is to postpone their capital purchase decisions and pay back debt to avoid bankruptcy. Our results provide empirical proof that Covid-19 had a significant impact on the debt levels of Indian companies. This bankruptcy-aversion behaviour could be troublesome for policymakers in developing economies recovering from the pandemic. Companies would need to be incentivized to raise capital for future growth. Increased government expenditure in the form of subsidies and loans during the initial phase of the pandemic received lacklustre response. Companies were uncertain about the extent and longevity of the crisis and hence put all expansion plans at bay. After almost two years since the first wave, there is now a general acceptance that business can go back to usual. Availability of cheaper forms of capital becomes key to boost investments. In this regard, the policymakers should look at alternative forms of raising debt, in addition to bank loans. The corporate bond market could be an attractive alternative. For a smoother revival of the economy, Indian policy makers should look at the proposals to deepen the corporate bond market.

7. Conclusion and limitations

Considering the importance of emerging economies to global trade, it is critical to ensure their access to cheaper sources of capital. Hence, further investigation into the leverage decisions of companies in emerging economies is essential. Traditional theories have tried to explain the factors influencing the optimal capital structure of companies in emerging economies. However, the literature is weak on how the preference between debt and equity of these companies changes during a crisis. The main objective of this study is to ascertain how the approach of companies towards debt in a prominent emerging economy changes during a global crisis such as Covid-19. The results of this study show that companies shift away from debt, thereby reducing the leverage ratio during a crisis. Despite the findings being significant in the Indian context, the authors are cognizant of the limitations of the study. The study focuses on an emerging economy; hence, the generalization of its results in the global context might not be relevant. Recent literature has also shown that the impact of the pandemic on capital structure depends on country characteristics. For instance, the dependence of an economy on debt financing influences the impact of crisis on capital structure. Hence, further research can focus on how country characteristics influence the financing decisions during a global crisis.

Variables used for analysis

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Acknowledgements

The authors would like to thank the reviewers for their valuable comments which helped them enhance the quality of the manuscript.

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Quantitative Finance > General Finance

Title: a study on impact of capital structure on profitability of companies listed in indian stock exchange with respect to automobile industry.

Abstract: Current research helps in understanding both positive and negative impacts of capital structure on profits of Indian automobile companies by using variables like Return on Capital Employed, Return on Long Term Funds, Return on Net Worth, Gross Profit Margin, and Operating Profit, and Return on Asset. The study hypothesized that RoCE, RoLT, and RoNW have a positive effect and GP, OP and ROA have a negative impact on debt-equity and interest coverage ratios i.e capital structure of the companies. Also, the study proves that the relationship between profitability and capital structure variables is strongly significant. The hypothesis was tested by using fixed effect and random effect models by considering 10 years of data (from 2010-2019) from 17 automobile companies. The result of the study recommends that the firms can improve their performance by using an optimal capital structure. Also, a fair mix of debt and equity should be established to ensure that the firm maintains capital adequacy. Firms can thus be able to meet their financial compulsions and investments that can promise attractive returns.

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• MUDRA: Journal of Finance and Accounting
• Vol 9 , Issue 1 , January - June 2022
• 10.17492/jpi.mudra.v9i1.912203

Impact of Capital Structure on Firm Profitability: A Study of Select Large-cap Auto Ancillary Companies in India

Vol 9 , Issue 1 , January - June 2022 | Pages: 36-55 | Research Paper  

Published Online: June 30, 2022

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The paper studies the impact of capital structure decisions on the firm profitability of select large-cap auto ancillary companies. The Indian auto ancillary industry is one of the crucial industries in India and contributes 2.3% to the total GDP. This sector employed 50 lakh people directly and indirectly in the year 2018-19. The study has been made on large-cap auto ancillary companies listed on the Bombay Stock Exchange (BSE). The study period ranges from 2010–2011 to 2020–2021. Ratio analysis and panel data analysis has been applied to perform the empirical analysis. It was found that the capital structure has a negative and significant impact on the firm profitability. Fixed Asset Turnover ratio was found to be a significant determinant of firm profitability of large-cap auto ancillary companies in India.

Capital structure; Profitability; Auto ancillary companies

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